A RAG project for your college: notes, syllabus and PYQs you can actually query

In a packed college auditorium
In a packed college auditorium

Originally published at https://pranjulrathour.scult.in/blog/rag-for-college-documents-project-idea. That copy is the canonical version and gets updates first.

When students ask me for a RAG project idea that is not "chat with a PDF", I point them at their own college: scattered notes, a syllabus PDF nobody reads, previous-year question papers in WhatsApp groups. A retrieval assistant over that corpus solves a real problem for real users you can interview — which is exactly what recruiters want to see.

Pranjul Rathour
Pranjul Rathour

Scope it to one department

Pick one semester of one course. Collect the syllabus, faculty notes (with permission), textbook chapter summaries you write yourself, and five years of question papers. A few hundred pages is plenty. Scoping small lets you finish, evaluate and iterate; a whole-university corpus is a thesis, not a project.

Architecture that will impress in an interview

  1. Ingest with structure: detect unit headings and question numbers; keep page and source metadata (see chunking strategies).
  2. Hybrid retrieval: dense embeddings plus BM25, because students search for exact terms like "Dijkstra" or "unit 3".
  3. A reranker and a confidence gate, so the assistant refuses when the syllabus does not cover a question.
  4. Citations to page and source, so a student can open the note it came from.
  5. A FastAPI backend with streaming, and a simple Next.js front end.

Features users will actually use

  • "Which units have appeared most in the last five years?" — answered from question-paper metadata, not the LLM's imagination.
  • "Explain this topic using our notes" — grounded explanation with page references.
  • "Generate a practice set for unit 2" — from real past questions, labelled by year.

How to evaluate and present it

Write 50 questions with known answers and measure recall and faithfulness the way I describe in how to evaluate a RAG system. Then put it in front of ten classmates for a week and record what they asked. In the interview, talk about the questions it refused and why — that is where you show judgement. Publish the repository with a README that leads with the evaluation numbers, not the tech stack.

On the mic
On the mic

This is the project I would want to see from a final-year student applying for a GenAI role: a real corpus, real users, and the honesty to show where it fails.

From my carousels
7 Levels of RAG Apps
7 Levels of RAG Apps, slide 17 Levels of RAG Apps, slide 2
7 Levels of RAG Apps, slide 37 Levels of RAG Apps, slide 4
Full carousel on Instagram and LinkedIn.
Pranjul Rathour
Pranjul Rathour
GenAI engineer, Kanpur · 3x first-prize hackathon winner · campus mentor
I ship production RAG pipelines, fine-tune LLMs and build agentic AI products end to end. I lead engineering at SCULT INDIA for a 14-member team and have mentored 200+ students through TechVerse Enclave.
Open to: GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges.
On stage, at hackathons and on campus
Presenting to a room
Presenting to a room
Pranjul Rathour
Pranjul Rathour
Pranjul Rathour, GenAI engineer, Kanpur
Pranjul Rathour, GenAI engineer, Kanpur

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